19,997 machine learning datasets
19,997 dataset results
he Center for Recognition and Inspection of Cells (CRIC) platform enables the creation of CRIC Cervix collection, currently with 400 images (1,376 x 1,020 pixels) curated from conventional Pap smears, with manual classification of 11,534 cells.
The dataset contains more than 35000 images and 600 videos captured using 35 different portable devices of 11 major brands. In addition to the original acquisitions, images were shared through Facebook and WhatsApp whereas videos were shared through YouTube and WhatsApp platforms.
To study kinship verification from gait, we collected the dataset KinGaitWild consisting of several videos from youtube. Most of the videos were taken under uncontrolled conditions in terms of background, camera motion, luminance and viewpoints. The KinGaitWild dataset contains 105 videos of celebrities and their relatives. The average time duration of each video is around 10 seconds. The database includes 60 pairs of Father-Son (FS) relationships. These pairs are equally split into 5 groups. We focus in this study on the Father-Son relationships. The database collection was done as follows. First, we used the YouTube Data API to search for videos showing celebrities walking in the wild. To avoid biases, we selected the pairs of celebrities so that the videos are not originated from the same source nor environment. For each video, we labeled the position of each specified person by a bounding box (bbox). These bboxes are used to estimate the human pose for silhouette-based approaches a
WMT 2021 Ge'ez-Amharic is a Ge'ez-Amharic dataset prepared for NMT tasks of the 6th Workshop on NLP at Debre Berhan University, Ethiopia. The corpus has been collected from:
Grévy’s Zebra is an animal pose estimation dataset for zebras.
Briefly describe the dataset. Provide:
We introduce a new data set containing 5000 scientific papers and their slides crawled from conference proceeding websites such as aclweb and usenix.
Dataset contains images with apple leaves infected by scab. The images are grouped in two folders: "Healthy" and "Scab". The collection of digital images were carried out in different locations of Latvia. Digital images with characteristic scab symptoms on leaves were collected by the Institute of Horticulture (LatHort) under project "lzp-2019/1-0094 Application of deep learning and datamining for the study of plant-pathogen interaction: the case of apple and pear scab" with a goal to create mobile application for apple scab detection using convolution neural networks. Devices: smartphone cameras (12 MP, 13 MP, 48 MP) and a digital compact camera (10 MP). The collection of images was carried out in field conditions, in orchards. The images were taken at three different stages of the day - in the morning (9:00-10:00), around noon (12:00-14:00), as well as in the evening (16:00-17:00) to provide a variety of natural light conditions. The images were also taken on both sunny days and over
Dataset contains images with apples infected by scab. The images are grouped in two folders: "Healthy" and "Scab". The collection of digital images were carried out in different locations of Latvia. Digital images with characteristic scab symptoms on fruits were collected by the Institute of Horticulture (LatHort) under project "lzp-2019/1-0094 Application of deep learning and datamining for the study of plant-pathogen interaction: the case of apple and pear scab" with a goal to create mobile application for apple scab detection using convolution neural networks. Devices: smartphone cameras (12 MP, 13 MP, 48 MP) and a digital compact camera (10 MP). The collection of images was carried out in field conditions, in orchards. The images were taken at three different stages of the day - in the morning (9:00-10:00), around noon (12:00-14:00), as well as in the evening (16:00-17:00) to provide a variety of natural light conditions. The images were also taken on both sunny days and overcast d
All existing databases of spoofed speech contain attack data that is spoofed in its entirety. In practice, it is entirely plausible that successful attacks can be mounted with utterances that are only partially spoofed. By definition, partially-spoofed utterances contain a mix of both spoofed and bona fide segments, which will likely degrade the performance of countermeasures trained with entirely spoofed utterances. This hypothesis raises the obvious question: ‘Can we detect partially spoofed audio?’ This paper introduces a new database of partially-spoofed data, named PartialSpoof, to help address this question. This new database enables to investigate and compare the performance of countermeasures on both utterance- and segmental- level labels.
Eduge news classification dataset provided by Bolorsoft LLC. Used to train the Eduge.mn production news classifier 75K news articles in 9 categories: урлаг соёл, эдийн засаг, эрүүл мэнд, хууль, улс төр, спорт, технологи, боловсрол and байгал орчин
Plankton was sampled with various nets, from bottom or 500m depth to the surface, in many oceans of the world. Samples were imaged with a ZooScan. The full images were processed with ZooProcess which generated regions of interest (ROIs) around each individual object and a set of associated features measured on the object (see Gorsky et al 2010 for more information). The same objects were re-processed to compute features with the scikit-image toolbox (http://scikit-image.org). The 1,433,278 resulting objects were sorted by a limited number of operators, following a common taxonomic guide, into 93 taxa, using the web application EcoTaxa (http://ecotaxa.obs-vlfr.fr).
This dataset, commissioned by the Yandex Business Directory, contains 10,000 photos of organization information signs shot in the Russian Federation along with the INN (taxpayer ID) and OGRN (Primary State Registration Number) codes shown on these signs. Toloka was used for both capturing photos and recognizing INN and OGRN codes.
This datase, contains 1244 images of hot and cold water meters as well as their readings and coordinates of the displays showing those readings. Each image contains exactly one water meter. The archive also includes the pictures of the results of segmentation with the masks and collages. Toloka was used for photo capturing, segmentation, and recognizing the readings.
Created as part of the Social Media Mining for Health Applications (#SMM4H '20) shared tasks, this dataset consists of 9515 tweets describing health issues. Each tweet is labeled for whether it contains information about an adverse side effect that occurred when taking a drug. The dataset was a joint effort with the UPenn HLP Center and the Chemoinformatics and Molecular Modeling Research Laboratory at Kazan Federal University.
This dataset contains the opinions of Russian native speakers about the relationship between a generic term (hypernym) and a specific instance of it (hyponym). Assembled by Dmitry Ustalov in 2017. A set of 300 most frequent nouns was extracted from the Russian National Corpus. Then each method or resource (including RuThes and RuWordNet) produced at most five hypernyms, if possible. This resulted in 10,600 unique non-empty subsumption pairs, which were annotated by seven different performers whose mother tongue is Russian and were at least 20 years old as of February 1, 2017. As a result, 4,576 out of 10,600 pairs were annotated as positive while the remaining 6,024 were annotated as negative. Interestingly, the performers were more confident in the negative answers than in the positive ones.
This dataset contains human-annotated sense identifiers for 2562 contexts of 20 words used in the RUSSE'2018 shared task on Word Sense Induction and Disambiguation for the Russian language. Assembled by Dmitry Ustalov in 2017. In particular, 80 pre-annotated contexts were used for training the human annotators, and 2562 contexts were annotated by humans such that each context was annotated by 9 different annotators. After the annotation, every context was additionally inspected (“curated”) by the organizers of the shared task.
The dataset collected at the University of Florence during 2012, has been captured using a Kinect camera. It includes 9 activities: wave, drink from a bottle, answer phone,clap, tight lace, sit down, stand up, read watch, bow. During acquisition, 10 subjects were asked to perform the above actions for 2/3 times. This resulted in a total of 215 activity samples.